3D model matching for fine pose determination

3D model matching is the process of matching image features to 3D model features. The feature correspondences are used to determine the transformation between the model and camera and thus, define the orientation of the object. The solution to this 3D model matching is difficult to calculate due to the high dimensionality of the solution space. Addressing this problem, we have developed a machine-vision system which incorporates an advanced iterative search procedure and Hough transform feature extraction into a system which avoids image segmentation to determine the fine pose of some objects. Inputs to the system include images of the object from various camera views, a coarse pose estimate and a CAD object model. A windowing grid system is applied to the scene image(s) and a single feature hypothesis is extracted from each window. A modified Newton-Raphson iterative search method is used to evaluate mappings between the image features and model features. The search attempts to optimize a cost function which is based on the perpendicular distance between linear features. Results have shown that the described fine pose determination method can achieve sub pixel accuracy when applied to images of various geometric shapes. The system has been applied to a number of applications including space rendezvous and docking, ordnance defusing, and missile tracking.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.